The technology for recommending items that are expected to be of interest to a user is widely implemented in various fields such as electronic commerce (EC), advertising on the Internet, and behavioral assistance services. Regarding item recommendation, the most popular method includes generating a matrix representing the correlation between the user and the items (or representing the level of interest shown by the user in the items), and performing statistical analysis based on the matrix. However, in this method, consideration is not given to the fact that the interest or needs of the user with respect to the items changes depending on the context. In that regard, in recent years, a method with regard to a restaurant recommendation task has been proposed in which context information such as the time slot, the occasion (holiday, birthday, anniversary, etc.), the location, and the accompanying person is additionally taken into account.
In the conventional method of recommending items with the use of context information, the usable context information is limited to the information that is easily observable from outside. However, in addition to the context information that is easily observable from outside, inner information, such as the objective or the mood, of the user that can be known only by asking him or her about the intentions is also believed to be useful during item recommendation. For that reason, there has been a demand for building a mechanism that enables estimation of inner information and recommendation of items with more accuracy.